The prediction of network latency with partial measurements is of importance for ever-increasing personal devices to ensure their Quality of Service (QoS). However, the current matrix-factorization-based efforts, as a promising paradigm, for network latency prediction have failed to intelligently exploit inherent factors hidden in networks to accurately infer the unknown network latency. Furthermore, it is more complicated to execute extensive network measurements on pervasive personal devices due to unstable communication environments. To alleviate these problems, in this paper, a novel accurate network latency prediction (DALP) solution via Deep Reinforcement Learning (DRL) is proposed for personal devices under cost-aware sampling. Specifically, we first alternately implement cost-aware latency measurement based on temporal correlation, and model it as a network latency matrix, in which unmeasured and missing elements need to be inferred. In order to achieve accurate prediction performance, the DRL-based Matrix Factorization with Double Weights (DWMF) is designed to exploit the potential network factors and multiple rules of matrix factorization, which can be alternatively executed, to minimize the prediction errors. Furthermore, an angle-loss-based reward strategy is designed to enhance the quality of model training. Simulation results on real-world datasets illustrate that DALP outperforms the previous approaches with quicker convergence and lower prediction errors.

A large number of distributed applications necessitate accurate network distance, for example, in the form of delay or latency, to ensure the Quality of Service (QoS). Due to high network measurement overhead and severe traffic congestion, network distance prediction has been introduced, instead of direct network measurements, to infer the unknown network distance with the partial measurements. However, most existing efforts neglect to fully capitalize on the potential latent factors, such as spatial correlations, long-existing temporal results and multi-rule exploration fusion, to achieve better accuracies with quicker convergence. To fill this gap, in this paper, we propose an Accurate Prediction of Network Distance (APND) solution via Federated Deep Reinforcement Learning (FDRL), which has four novel features distinguishing from the previous work. Firstly, a local feature-based matrix with low rank is established in each network cluster, referring to a set of neighbor nodes, to represent the potential spatial correlations among reachable node-pairs. Secondly, the parallel FDRL-based matrix factorization with multi-rule exploration fusion is introduced into APND and executed in all local clusters to minimize prediction errors and accelerate learning convergence. Thirdly, the long-existing learning experience is designed for local model training via Deep Reinforcement Learning (DRL) with rapid convergence. Fourthly, following the real-world routing paths, the cross-domain network nodes are simultaneously classified into adjacent clusters, built on the spatial correlations among them, and their coordinates will be further refined with error-based and average-based policies. Extensive experiments built on available real-world datasets illustrate that APND can accurately predict network distance compared with state-of-the-art approaches at the moderate computing cost.

Brain decoding, understood as the process of mapping brain activities to the stimuli that generated them, has been an active research area in the last years. In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading. However, such word embeddings are designed for natural language processing tasks rather than for brain decoding. Therefore, they limit the model’s ability to recover the precise stimulus. In this work, we propose to directly classify an fMRI scan, mapping it to the corresponding word within a fixed vocabulary. Unlike existing work, we evaluate on scans from previously unseen subjects. We argue that this is a more realistic setup and we present a model that can decode fMRI data from unseen subjects with 2.62% Top-1 and 9.76% Top5 accuracy in this challenging task. Moreover our model can be fine-tuned on data from the test subject to achieve 4.22% Top-1 and 12.87% Top-5 accuracy, significantly outperforming all the considered competitive baselines.